Title:Adaptive Generation of Unrestricted Adversarial Inputs

Abstract: Neural networks are vulnerable to adversarially-constructed perturbations of
their inputs. Most research so far has considered perturbations of a fixed
magnitude under some $l_p$ norm. Although studying these attacks is valuable,
there has been increasing interest in the construction of (and robustness to)
unrestricted attacks, which are not constrained to a small and rather
artificial subset of all possible adversarial inputs. We introduce a novel
algorithm for generating such unrestricted adversarial inputs which, unlike
prior work, is adaptive: it is able to tune its attacks to the classifier being
targeted. It also offers a 400-2,000x speedup over the existing state of the
art. We demonstrate our approach by generating unrestricted adversarial inputs
that fool classifiers robust to perturbation-based attacks. We also show that,
by virtue of being adaptive and unrestricted, our attack is able to defeat
adversarial training against it.